Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. This paper reports on first results of a research program that uses partially observable Markov models to robustly track a robot's location in office environments and to direct its goal-oriented actions. The approach explicitly maintains a probability distribution over the possible locations of the robot, taking into account various sources of uncertainty, including approximate knowledge of the environment, and actuator and sensor uncertainty. A novel feature of our approach is its integration of topological map information with approximate metric information. We demonstrate the robustness of this approach in controlling an actual indoor mobile robot navigating corridors. 1 Introduction We are interested in the task of long-term autonomous navigation in an office environment (with corridors, foyers, and rooms). While the state of the art in autonomous office nav...

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This paper presents a method that uses the level sets of volumes to reconstruct the shapes of 3D objects from range data. The strategy is to formulate 3D reconstruction as a statistical problem: find that surface which is mostly likely, given the data and some prior knowledge about the application domain. The resulting optimization problem is solved by an incremental process of deformation. We represent a deformable surface as the level set of a discretely sampled scalar function of 3 dimensions, i.e. a volume. Such level-set models have been shown to mimic conventional deformable surface models by encoding surface movements as changes in the greyscale values of the volume. The result is a voxel-based modeling technology that offers several advantages over conventional parametric models, including flexible topology, no need for reparameterization, concise descriptions of differential structure, and a natural scale space for hierarchical representations. This paper builds on previous work in both 3D reconstruction and level-set modeling. It presents a fundamental result in surface estimation from range data: an analytical characterization of the surface that maximizes the posterior probability. It also presents a novel computational technique for level-set modeling, called the sparse-field algorithm, which combines the advantages of a level-set approach with the computational efficiency and accuracy of a parametric representation. The sparse-field algorithm is more efficient than other approaches, and because it assigns the level set to a specific set of grid points, it positions the level-set model more accurately than the grid itself. These properties, computational efficiency and sub-cell accuracy, are essential when trying to reconstruct the shapes of 3D objects. Results are shown for the reconstruction objects from sets of noisy and overlapping range maps.

. This paper addresses the problem of perception and localisation for a mobile robot in an unknown environment. It describes a modelling of the environment based on two certainty grids: one local model and the other one a global model. Localisation of the robot is reduced to finding the best alignment of the local certainty grid onto the global certainty grid. Four localisation procedures to correct the robot position are introduced. First experimental results show the capacity of the method. 1 Introduction Certainty Grids have been proposed as a way to construct an internal model of static environments based on sensor data ([Mor 85, Mor 88]). This method takes into account the uncertainty of sensory data by working with probabilities or certainty values. The certainty grid representation can be used directly in robotic planning ([Wal 92]) or navigation ([Elf 89]). Other authors have used a certainty grid method for collision avoidance ([Bor 91, Bor 90]). Two drawbacks of the certaint...

Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. We present a technique for achieving this goal that uses partially observable Markov decision process models (POMDPs) to explicitly model navigation uncertainty, including actuator and sensor uncertainty and approximate knowledge of the environment. This allows the robot to maintain a probability distribution over its current pose. Thus, while the robot rarely knows exactly where it is, it always has some belief as to what its true pose is, and is never completely lost. We present a navigation architecture based on POMDPs that provides a uniform framework with an established theoretical foundation for pose estimation, path planning, robot control during navigation, and learning. Our experiments show that this architecture indeed leads to robust corridor navigation for an actual indoor mobile robot. 1

This paper presents further developments of the earlier Vector Field Histogram (VFH) method for realtime mobile robot obstacle avoidance. The enhanced method, called VFH+, offers several improvements that result in smoother robot trajectories and greater reliability. VFH+ reduces some of the parameter tuning of the original VFH method by explicitly compensating for the robot width. Also added in VFH+ is a better approximation of the mobile robot trajectory, which results in higher reliability. 1. INTRODUCTION The VFH+ method is an improved version of the Vector Field Histogram (VFH) method originally developed by Borenstein and Koren [1991] for real-time, local obstacle avoidance with mobile robots. VFH+ was developed for a special type of mobile robot called the GuideCane. The GuideCane, shown in Figure 1, is a novel guidance device for the blind. In operation, a blind user pushes the unpowered GuideCane ahead of himself. When the GuideCane encounters an obstacle it steers a...

Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. We have developed an approach that uses partially observable Markov models to robustly track a robot’s location and integrates it with a planning and execution monitoring approach that uses this information to control the robot’s actions. The approach explicitly maintains a probability distributionover the possiblelocations of the robot, taking into account various sources of uncertainty, including approximate knowledge of the environment, actuator uncertainty, and sensor noise. A novel feature of our approach is its integration of topological map information with approximate metric information. We demonstrate the reliability of this approach, especially its ability to smoothly recover from errors in sensing. 1.

This paper introduces histogramic in-motion mapping(HIMM), a new method for real-time map building with a mobile robot in motion. HIMM represents data in a two-dimensional array, called a histogram grid, that is updated through rapid in-motion sampling of onboard range sensors. Rapid in-motion sampling results in a map representation that is well-suited to modeling inaccurate and noisy range-sensor data, such as that produced by ultrasonic sensors, and requires minimal computational overhead. Fast map-building allows the robot to immediately use the mapped information in real-time obstacle-avoidance algorithms. The benefits of this integrated approach are twofold: (1) quick, accurate mapping; and (2) safe navigation of the robot toward a given target. HIMM has been implemented and tested on a mobile robot. Its dual functionality was demonstrated through numerous tests in which maps of unknown obstacle courses were created, while the robot simultaneously performed real-time obstacle avoidance maneuvers at speeds of up to 0.78 m/sec.

This paper describes a working stereo-vision-based mobile robot that can navigate and autonomously explore its environment safely while building occupancy grid maps of the environment. We present a method for reducing stereo vision disparity images to two dimensional map information. Stereo vision has several attributes that set it apart from other sensors more commonly used for occupancy grid mapping. We discuss these attributes, the errors that some of them create, and how to overcome them. This includes the idea of segmenting disparity images based on continuous disparity surfaces to reject "spikes" caused by stereo mismatches. Stereo vision processing and map updates are done at 5Hz and the robot moves at speeds of 150 cm/s.

Abstract. We describe a method called Predestination that uses a history of a driver’s destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses. Driving behaviors include types of destinations, driving efficiency, and trip times. Beyond considering previously visited destinations, Predestination leverages an open-world modeling methodology that considers the likelihood of users visiting previously unobserved locations based on trends in the data and on the background properties of locations. This allows our algorithm to smoothly transition between “out of the box ” with no training data to more fully trained with increasing numbers of observations. Multiple components of the analysis are fused via Bayesian inference to produce a probabilistic map of destinations. Our algorithm was trained and tested on hold-out data drawn from a database of GPS driving data gathered from 169 different subjects who drove 7,335 different trips. 1